Hybrid Querying Over Relational Databases and Large Language Models
Summary: Presents SWAN: the first cross-domain benchmark of 120 beyond-database questions over four real-world relational schemas for hybrid DB+LLM querying. Proposes schema-expansion and UDF-based integration, evaluates GPT‑4 Turbo (≤40% exec accuracy, 48.2% factuality) and exposes optimization needs and accuracy/factuality gaps. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fuheng Zhao (University of California Santa Barbara)
- 2. Divyakant Agrawal (University of California Santa Barbara)
- 3. Amr El Abbadi (University of California Santa Barbara)
BibTeX Citation
@inproceedings{zhao_cidr25,
address = {Amsterdam, Netherlands},
series = {{CIDR} '25},
title = {{Hybrid Querying Over Relational Databases and Large Language Models}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Zhao, Fuheng and Agrawal, Divyakant and Abbadi, Amr El},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,743 | Logical and Physical Optimizations for SQL Query Execution over Large Language Models | 2025 | SIGMOD | 7.0586112e-05 |
| 9,304 | Sphinteract: Resolving Ambiguities in NL2SQL Through User Interaction | 2025 | VLDB | 5.1978532e-05 |
| 11,024 | Bridging LLMs and Database Systems: A Deep Dive into Enhanced Relational Operators | 2026 | VLDB | 4.9793485e-05 |
| 11,161 | ScaleLLM: A Technique for Scalable LLM-augmented Data Systems | 2025 | SIGMOD | 4.9793485e-05 |
| 11,165 | SwellDB: Dynamic Query-Driven Table Generation with Large Language Models | 2025 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 71 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD | 0.00037720227 |
| 92 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00034672523 |
| 174 | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | 2024 | VLDB | 0.00026790979 |
| 257 | Crowdsourced Databases: Query Processing with People | 2011 | CIDR | 0.00022962347 |
| 259 | Answering Queries using Humans, Algorithms and Databases | 2011 | CIDR | 0.00022923243 |
| 329 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00020858443 |
| 2,117 | NL2SQL is a solved problem... Not! | 2024 | CIDR | 9.0149494e-05 |
| 2,156 | Obtaining Complete Answers from Incomplete Databases | 1996 | VLDB | 8.9455055e-05 |
| 2,429 | Deco: A System for Declarative Crowdsourcing | 2012 | VLDB | 8.4801295e-05 |
| 3,695 | Revisiting Prompt Engineering via Declarative Crowdsourcing | 2024 | CIDR | 7.092445e-05 |
| 8,970 | What Should A Database Know? | 1988 | PODS | 5.2466122e-05 |
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